br Introduction The obesity epidemic has
Introduction The obesity epidemic has been linked to a web of interdependent causes operating at multiple cascading levels (Galea, Riddle, & Kaplan, 2010; Glass & McAtee, 2006; Huang, Drewnosksi, Kumanyika, & Glass, 2009) including environmental influences, genetics, cultural preferences, environmental cues, food pricing and availability, and peer influence (Myers & Rosen, 1999). These complex relationships have been widely studied using conventional study designs and regression-based models. However, it is increasingly understood that obesity is an outgrowth of complex dynamic processes at multiple levels that demonstrate non-linear features such as feedback loops and endogenous peer influences that are not well-captured using conventional approaches (Finegood, 2012; Finegood & Cawley, 2011; Galea et al., 2010; Hammond & Dubé, 2012; Huang & Glass, 2008; Ip, Rahmandad, Shoham, Hammond, & Huang, 2013). The complexity of the obesity epidemic has drawn attention from researchers from a wide range of disciplines seeking new strategies to study the drivers of and solutions to the epidemic. Therefore, increasingly, agent-based computational models (ABMs) have been explored as an alternative approach for addressing scientific and policy questions and as a focal point for collaborations of multidisciplinary teams. Agent-based models are computational simulations of real-world dynamic patterns of adaptive behavior (Auchincloss & Diez Roux, 2008; Bonabeau, 2002; Gilbert & Troitzsch, 2005). Their principal strength is the ability to model and capture emergent collective behavior arising from dynamic EVP-6124 of knowledgeable actors who seek strategic solutions in the face of environmental constraints and whose complex interactions create emergent patterns that cannot be predicted or understood using conventional methods that do not permit non-linear dynamics (Epstein, 2006; Epstein & Axtell, 1996; Macy & Willer, 2002; Maglio & Mabry, 2011). In obesity research, ABMs have been used previously to understand the role of the food and physical activity (PA) environments (Auchincloss & Diez Roux, 2008; Widener, Metcalf, & Bar-Yam, 2013; Yang, Diez Roux, Auchincloss, Rodriguez, & Brown, 2011; Yang & Diez-Roux, 2013), social norms (Auchincloss, Riolo, Brown, Cook, & Diez Roux, 2011; Hammond & Ornstein, 2014; Mooney & El-Sayed, 2014; Shoham, Tong, Lamberson, Auchincloss, & Zhang, 2012; Wang, Xue, Chen, & Igusa, 2014), network and peer effects (El-Sayed, Scarborough, Seemann, & Galea, 2012; Hammond & Ornstein, 2014; Shoham et al., 2012; Trogdon & Allaire, 2014), and diffusion of interventions (El-Sayed, Seemann, Scarborough, & Galea, 2013; Rahmandad & Sterman, 2008; Widener et al., 2013; Zhang, Giabbanelli, Arah, & Zimmerman, 2014). It is this last application that is our principal focus, to which we now turn. A central challenge in public health response to the obesity epidemic is the lack of consensus about the optimal strategy for targeting intervention resources. While behavioral interventions to prevent and reduce pathogenic weight gain in various populations have proven difficult, there are strategies that have been tested and found to be, to varying degrees, efficacious. These include interventions to reduce caloric intake and increase physical activity over a sustained period for purposes of weight reduction or obesity prevention. For instance, given a fixed pool of available resources, policy makers, program managers, and other decision makers must decide how to target resources to achieve the maximum desired benefit across a target population. Given a behavioral intervention of fixed efficacy and fixed cost per person (on average), should we target those who are obese, those who live in high-risk areas, or choose at random? This is an ideal problem for agent-based simulation models that can be used to conduct counterfactual experiments to test alternative targeting strategies (El-Sayed et al., 2013). This approach has been effective in tobacco. For example, Levy used a simulation model to show that targeting youth smokers results in limited impact compared to targeting all age groups (Levy, Cummings, & Hyland, 2000).